Overview

Dataset statistics

Number of variables23
Number of observations3803
Missing cells6996
Missing cells (%)8.0%
Duplicate rows126
Duplicate rows (%)3.3%
Total size in memory683.5 KiB
Average record size in memory184.0 B

Variable types

Categorical10
Text3
Numeric10

Alerts

Dataset has 126 (3.3%) duplicate rowsDuplicates
price is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price and 7 other fieldsHigh correlation
built_up_area is highly overall correlated with price and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 2 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
store room is highly imbalanced (56.2%)Imbalance
facing has 1105 (29.1%) missing valuesMissing
super_built_up_area has 1888 (49.6%) missing valuesMissing
built_up_area has 2070 (54.4%) missing valuesMissing
carpet_area has 1859 (48.9%) missing valuesMissing
area is highly skewed (γ1 = 30.23273447)Skewed
built_up_area is highly skewed (γ1 = 41.21758008)Skewed
carpet_area is highly skewed (γ1 = 24.7960836)Skewed
floorNum has 134 (3.5%) zerosZeros
luxury_score has 486 (12.8%) zerosZeros

Reproduction

Analysis started2023-10-23 15:47:52.035401
Analysis finished2023-10-23 15:48:27.043426
Duration35.01 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
flat
2943 
house
860 

Length

Max length5
Median length4
Mean length4.2261373
Min length4

Characters and Unicode

Total characters16072
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2943
77.4%
house 860
 
22.6%

Length

2023-10-23T15:48:27.222932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:27.515910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
flat 2943
77.4%
house 860
 
22.6%

Most occurring characters

ValueCountFrequency (%)
f 2943
18.3%
l 2943
18.3%
a 2943
18.3%
t 2943
18.3%
h 860
 
5.4%
o 860
 
5.4%
u 860
 
5.4%
s 860
 
5.4%
e 860
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16072
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2943
18.3%
l 2943
18.3%
a 2943
18.3%
t 2943
18.3%
h 860
 
5.4%
o 860
 
5.4%
u 860
 
5.4%
s 860
 
5.4%
e 860
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 16072
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2943
18.3%
l 2943
18.3%
a 2943
18.3%
t 2943
18.3%
h 860
 
5.4%
o 860
 
5.4%
u 860
 
5.4%
s 860
 
5.4%
e 860
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2943
18.3%
l 2943
18.3%
a 2943
18.3%
t 2943
18.3%
h 860
 
5.4%
o 860
 
5.4%
u 860
 
5.4%
s 860
 
5.4%
e 860
 
5.4%
Distinct676
Distinct (%)17.8%
Missing1
Missing (%)< 0.1%
Memory size29.8 KiB
2023-10-23T15:48:27.961054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.922672
Min length1

Characters and Unicode

Total characters64340
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique291 ?
Unique (%)7.7%

Sample

1st rowsignature global park 4
2nd rowsmart world gems
3rd rowpyramid elite
4th rowbreez global hill view
5th rowbestech park view sanskruti
ValueCountFrequency (%)
independent 491
 
4.9%
the 362
 
3.6%
dlf 225
 
2.2%
park 219
 
2.2%
city 172
 
1.7%
global 165
 
1.6%
signature 161
 
1.6%
emaar 159
 
1.6%
m3m 156
 
1.6%
heights 139
 
1.4%
Other values (783) 7779
77.6%
2023-10-23T15:48:28.992773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6935
 
10.8%
6228
 
9.7%
a 6090
 
9.5%
r 4355
 
6.8%
n 4270
 
6.6%
i 3970
 
6.2%
t 3851
 
6.0%
s 3627
 
5.6%
l 3074
 
4.8%
o 2867
 
4.5%
Other values (31) 19073
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57544
89.4%
Space Separator 6228
 
9.7%
Decimal Number 550
 
0.9%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6935
12.1%
a 6090
 
10.6%
r 4355
 
7.6%
n 4270
 
7.4%
i 3970
 
6.9%
t 3851
 
6.7%
s 3627
 
6.3%
l 3074
 
5.3%
o 2867
 
5.0%
d 2550
 
4.4%
Other values (16) 15955
27.7%
Decimal Number
ValueCountFrequency (%)
3 216
39.3%
2 83
 
15.1%
1 76
 
13.8%
6 62
 
11.3%
8 35
 
6.4%
4 19
 
3.5%
5 17
 
3.1%
9 15
 
2.7%
7 14
 
2.5%
0 13
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6228
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57544
89.4%
Common 6796
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6935
12.1%
a 6090
 
10.6%
r 4355
 
7.6%
n 4270
 
7.4%
i 3970
 
6.9%
t 3851
 
6.7%
s 3627
 
6.3%
l 3074
 
5.3%
o 2867
 
5.0%
d 2550
 
4.4%
Other values (16) 15955
27.7%
Common
ValueCountFrequency (%)
6228
91.6%
3 216
 
3.2%
2 83
 
1.2%
1 76
 
1.1%
6 62
 
0.9%
8 35
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 15
 
0.2%
7 14
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6935
 
10.8%
6228
 
9.7%
a 6090
 
9.5%
r 4355
 
6.8%
n 4270
 
6.6%
i 3970
 
6.2%
t 3851
 
6.0%
s 3627
 
5.6%
l 3074
 
4.8%
o 2867
 
4.5%
Other values (31) 19073
29.6%

sector
Text

Distinct113
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:29.721303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3234289
Min length7

Characters and Unicode

Total characters35457
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 36
2nd rowsector 89
3rd rowsector 86
4th rowsohna road
5th rowsector 92
ValueCountFrequency (%)
sector 3569
46.7%
road 187
 
2.4%
sohna 175
 
2.3%
102 113
 
1.5%
85 110
 
1.4%
92 105
 
1.4%
69 94
 
1.2%
90 91
 
1.2%
65 90
 
1.2%
81 90
 
1.2%
Other values (106) 3012
39.4%
2023-10-23T15:48:30.883574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3942
11.1%
3833
10.8%
s 3824
10.8%
r 3823
10.8%
e 3660
10.3%
c 3622
10.2%
t 3580
10.1%
1 1105
 
3.1%
0 827
 
2.3%
8 808
 
2.3%
Other values (21) 6433
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24106
68.0%
Decimal Number 7518
 
21.2%
Space Separator 3833
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3942
16.4%
s 3824
15.9%
r 3823
15.9%
e 3660
15.2%
c 3622
15.0%
t 3580
14.9%
a 730
 
3.0%
d 263
 
1.1%
n 230
 
1.0%
h 213
 
0.9%
Other values (10) 219
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1105
14.7%
0 827
11.0%
8 808
10.7%
9 805
10.7%
6 760
10.1%
7 707
9.4%
3 699
9.3%
2 699
9.3%
5 608
8.1%
4 500
6.7%
Space Separator
ValueCountFrequency (%)
3833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24106
68.0%
Common 11351
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3942
16.4%
s 3824
15.9%
r 3823
15.9%
e 3660
15.2%
c 3622
15.0%
t 3580
14.9%
a 730
 
3.0%
d 263
 
1.1%
n 230
 
1.0%
h 213
 
0.9%
Other values (10) 219
 
0.9%
Common
ValueCountFrequency (%)
3833
33.8%
1 1105
 
9.7%
0 827
 
7.3%
8 808
 
7.1%
9 805
 
7.1%
6 760
 
6.7%
7 707
 
6.2%
3 699
 
6.2%
2 699
 
6.2%
5 608
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3942
11.1%
3833
10.8%
s 3824
10.8%
r 3823
10.8%
e 3660
10.3%
c 3622
10.2%
t 3580
10.1%
1 1105
 
3.1%
0 827
 
2.3%
8 808
 
2.3%
Other values (21) 6433
18.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct473
Distinct (%)12.5%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5058045
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:31.422334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.94
median1.5
Q32.7
95-th percentile8.49
Maximum31.5
Range31.43
Interquartile range (IQR)1.76

Descriptive statistics

Standard deviation2.9501212
Coefficient of variation (CV)1.177315
Kurtosis15.257819
Mean2.5058045
Median Absolute Deviation (MAD)0.71
Skewness3.3113347
Sum9484.47
Variance8.703215
MonotonicityNot monotonic
2023-10-23T15:48:31.840139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 83
 
2.2%
0.9 68
 
1.8%
1.5 66
 
1.7%
1.2 66
 
1.7%
1.1 66
 
1.7%
1.4 63
 
1.7%
1.3 60
 
1.6%
0.95 58
 
1.5%
2 56
 
1.5%
1 51
 
1.3%
Other values (463) 3148
82.8%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 9
0.2%
0.21 6
0.2%
0.22 9
0.2%
0.23 1
 
< 0.1%
0.24 7
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2651
Distinct (%)70.0%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13800.168
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:32.136887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4718.2
Q16808
median9000
Q313765
95-th percentile33308.2
Maximum600000
Range599996
Interquartile range (IQR)6957

Descriptive statistics

Standard deviation23052.006
Coefficient of variation (CV)1.6704149
Kurtosis187.04187
Mean13800.168
Median Absolute Deviation (MAD)2758
Skewness11.43922
Sum52233635
Variance5.3139496 × 108
MonotonicityNot monotonic
2023-10-23T15:48:32.413209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 28
 
0.7%
8000 19
 
0.5%
12500 17
 
0.4%
5000 17
 
0.4%
7500 14
 
0.4%
6666 14
 
0.4%
11111 14
 
0.4%
22222 13
 
0.3%
8333 13
 
0.3%
33333 11
 
0.3%
Other values (2641) 3625
95.3%
(Missing) 18
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)34.7%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2845.9995
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:32.727289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11220
median1725
Q32295
95-th percentile4200
Maximum875000
Range874950
Interquartile range (IQR)1075

Descriptive statistics

Standard deviation22783.349
Coefficient of variation (CV)8.0053947
Kurtosis974.19183
Mean2845.9995
Median Absolute Deviation (MAD)525
Skewness30.232734
Sum10772108
Variance5.1908099 × 108
MonotonicityNot monotonic
2023-10-23T15:48:33.023727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 55
 
1.4%
1350 51
 
1.3%
1800 48
 
1.3%
1950 44
 
1.2%
3240 43
 
1.1%
900 39
 
1.0%
2700 39
 
1.0%
2000 35
 
0.9%
2400 25
 
0.7%
2250 25
 
0.7%
Other values (1302) 3381
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:33.534490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.841967
Min length12

Characters and Unicode

Total characters204761
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1780 ?
Unique (%)46.8%

Sample

1st rowSuper Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)
2nd rowCarpet area: 1103 (102.47 sq.m.)
3rd rowCarpet area: 58141 (5401.48 sq.m.)
4th rowBuilt Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)
5th rowSuper Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)
ValueCountFrequency (%)
area 5728
18.5%
sq.m 3779
12.2%
up 3102
 
10.0%
built 2393
 
7.7%
super 1915
 
6.2%
sq.ft 1779
 
5.7%
sq.m.)carpet 1208
 
3.9%
carpet 732
 
2.4%
sq.m.)built 707
 
2.3%
plot 682
 
2.2%
Other values (2846) 8965
28.9%
2023-10-23T15:48:34.438822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27187
 
13.3%
. 20907
 
10.2%
a 13536
 
6.6%
r 9723
 
4.7%
e 9587
 
4.7%
1 9460
 
4.6%
s 7747
 
3.8%
q 7611
 
3.7%
t 7507
 
3.7%
p 6961
 
3.4%
Other values (25) 84535
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84919
41.5%
Decimal Number 48415
23.6%
Space Separator 27187
 
13.3%
Other Punctuation 24038
 
11.7%
Uppercase Letter 8830
 
4.3%
Close Punctuation 5686
 
2.8%
Open Punctuation 5686
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13536
15.9%
r 9723
11.4%
e 9587
11.3%
s 7747
9.1%
q 7611
9.0%
t 7507
8.8%
p 6961
8.2%
u 6932
8.2%
m 5696
6.7%
l 3784
 
4.5%
Other values (5) 5835
6.9%
Decimal Number
ValueCountFrequency (%)
1 9460
19.5%
0 6789
14.0%
2 5850
12.1%
5 4855
10.0%
3 4071
8.4%
4 3809
7.9%
6 3772
 
7.8%
7 3343
 
6.9%
8 3238
 
6.7%
9 3228
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3102
35.1%
C 1944
22.0%
S 1915
21.7%
U 1187
 
13.4%
P 682
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 20907
87.0%
: 3131
 
13.0%
Space Separator
ValueCountFrequency (%)
27187
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5686
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111012
54.2%
Latin 93749
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13536
14.4%
r 9723
10.4%
e 9587
10.2%
s 7747
8.3%
q 7611
8.1%
t 7507
8.0%
p 6961
7.4%
u 6932
7.4%
m 5696
 
6.1%
l 3784
 
4.0%
Other values (10) 14665
15.6%
Common
ValueCountFrequency (%)
27187
24.5%
. 20907
18.8%
1 9460
 
8.5%
0 6789
 
6.1%
2 5850
 
5.3%
) 5686
 
5.1%
( 5686
 
5.1%
5 4855
 
4.4%
3 4071
 
3.7%
4 3809
 
3.4%
Other values (5) 16712
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27187
 
13.3%
. 20907
 
10.2%
a 13536
 
6.6%
r 9723
 
4.7%
e 9587
 
4.7%
1 9460
 
4.6%
s 7747
 
3.8%
q 7611
 
3.7%
t 7507
 
3.7%
p 6961
 
3.4%
Other values (25) 84535
41.3%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3381541
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:34.732335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8767336
Coefficient of variation (CV)0.56220699
Kurtosis18.610254
Mean3.3381541
Median Absolute Deviation (MAD)1
Skewness3.511539
Sum12695
Variance3.5221288
MonotonicityNot monotonic
2023-10-23T15:48:35.083592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1545
40.6%
2 993
26.1%
4 676
17.8%
5 213
 
5.6%
1 130
 
3.4%
6 75
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.7%
7 28
 
0.7%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 130
 
3.4%
2 993
26.1%
3 1545
40.6%
4 676
17.8%
5 213
 
5.6%
6 75
 
2.0%
7 28
 
0.7%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.7%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4054694
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:35.332895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9304562
Coefficient of variation (CV)0.56686936
Kurtosis17.745175
Mean3.4054694
Median Absolute Deviation (MAD)1
Skewness3.2570832
Sum12951
Variance3.7266613
MonotonicityNot monotonic
2023-10-23T15:48:35.566926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1112
29.2%
2 1105
29.1%
4 839
22.1%
5 299
 
7.9%
1 160
 
4.2%
6 120
 
3.2%
7 41
 
1.1%
9 41
 
1.1%
8 26
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 160
 
4.2%
2 1105
29.1%
3 1112
29.2%
4 839
22.1%
5 299
 
7.9%
6 120
 
3.2%
7 41
 
1.1%
8 26
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
3+
1202 
3
1110 
2
925 
1
376 
0
190 

Length

Max length2
Median length1
Mean length1.3160663
Min length1

Characters and Unicode

Total characters5005
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1202
31.6%
3 1110
29.2%
2 925
24.3%
1 376
 
9.9%
0 190
 
5.0%

Length

2023-10-23T15:48:35.953210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:36.234436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2312
60.8%
2 925
24.3%
1 376
 
9.9%
0 190
 
5.0%

Most occurring characters

ValueCountFrequency (%)
3 2312
46.2%
+ 1202
24.0%
2 925
18.5%
1 376
 
7.5%
0 190
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
76.0%
Math Symbol 1202
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2312
60.8%
2 925
24.3%
1 376
 
9.9%
0 190
 
5.0%
Math Symbol
ValueCountFrequency (%)
+ 1202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2312
46.2%
+ 1202
24.0%
2 925
18.5%
1 376
 
7.5%
0 190
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2312
46.2%
+ 1202
24.0%
2 925
18.5%
1 376
 
7.5%
0 190
 
3.8%

floorNum
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)1.1%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.8102537
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:36.553987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0275551
Coefficient of variation (CV)0.88507056
Kurtosis4.5493229
Mean6.8102537
Median Absolute Deviation (MAD)3
Skewness1.6987333
Sum25770
Variance36.33142
MonotonicityNot monotonic
2023-10-23T15:48:36.872597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 513
13.5%
2 506
13.3%
1 365
 
9.6%
4 328
 
8.6%
8 197
 
5.2%
6 187
 
4.9%
10 186
 
4.9%
7 183
 
4.8%
5 177
 
4.7%
9 170
 
4.5%
Other values (33) 972
25.6%
ValueCountFrequency (%)
0 134
 
3.5%
1 365
9.6%
2 506
13.3%
3 513
13.5%
4 328
8.6%
5 177
 
4.7%
6 187
 
4.9%
7 183
 
4.8%
8 197
 
5.2%
9 170
 
4.5%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 2
0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1105
Missing (%)29.1%
Memory size29.8 KiB
East
642 
North-East
639 
North
398 
West
255 
South
233 
Other values (3)
531 

Length

Max length10
Median length5
Mean length6.8358043
Min length4

Characters and Unicode

Total characters18443
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-West
2nd rowNorth-East
3rd rowNorth-East
4th rowEast
5th rowNorth-East

Common Values

ValueCountFrequency (%)
East 642
16.9%
North-East 639
16.8%
North 398
 
10.5%
West 255
 
6.7%
South 233
 
6.1%
North-West 200
 
5.3%
South-East 174
 
4.6%
South-West 157
 
4.1%
(Missing) 1105
29.1%

Length

2023-10-23T15:48:37.142777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:37.445799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
east 642
23.8%
north-east 639
23.7%
north 398
14.8%
west 255
 
9.5%
south 233
 
8.6%
north-west 200
 
7.4%
south-east 174
 
6.4%
south-west 157
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3868
21.0%
s 2067
11.2%
o 1801
9.8%
h 1801
9.8%
E 1455
 
7.9%
a 1455
 
7.9%
N 1237
 
6.7%
r 1237
 
6.7%
- 1170
 
6.3%
W 612
 
3.3%
Other values (3) 1740
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13405
72.7%
Uppercase Letter 3868
 
21.0%
Dash Punctuation 1170
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3868
28.9%
s 2067
15.4%
o 1801
13.4%
h 1801
13.4%
a 1455
 
10.9%
r 1237
 
9.2%
e 612
 
4.6%
u 564
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 1455
37.6%
N 1237
32.0%
W 612
15.8%
S 564
 
14.6%
Dash Punctuation
ValueCountFrequency (%)
- 1170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17273
93.7%
Common 1170
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3868
22.4%
s 2067
12.0%
o 1801
10.4%
h 1801
10.4%
E 1455
 
8.4%
a 1455
 
8.4%
N 1237
 
7.2%
r 1237
 
7.2%
W 612
 
3.5%
e 612
 
3.5%
Other values (2) 1128
 
6.5%
Common
ValueCountFrequency (%)
- 1170
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3868
21.0%
s 2067
11.2%
o 1801
9.8%
h 1801
9.8%
E 1455
 
7.9%
a 1455
 
7.9%
N 1237
 
6.7%
r 1237
 
6.7%
- 1170
 
6.3%
W 612
 
3.3%
Other values (3) 1740
9.4%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
Relatively New
1676 
New Property
626 
Moderately Old
575 
Undefined
333 
Old Property
310 

Length

Max length18
Median length14
Mean length13.367605
Min length9

Characters and Unicode

Total characters50837
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowNew Property
3rd rowUnder Construction
4th rowNew Property
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1676
44.1%
New Property 626
 
16.5%
Moderately Old 575
 
15.1%
Undefined 333
 
8.8%
Old Property 310
 
8.2%
Under Construction 283
 
7.4%

Length

2023-10-23T15:48:37.714820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:38.001307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
new 2302
31.7%
relatively 1676
23.0%
property 936
12.9%
old 885
 
12.2%
moderately 575
 
7.9%
undefined 333
 
4.6%
under 283
 
3.9%
construction 283
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e 8689
17.1%
l 4812
 
9.5%
t 3753
 
7.4%
3470
 
6.8%
y 3187
 
6.3%
r 3013
 
5.9%
d 2409
 
4.7%
N 2302
 
4.5%
w 2302
 
4.5%
i 2292
 
4.5%
Other values (15) 14608
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40094
78.9%
Uppercase Letter 7273
 
14.3%
Space Separator 3470
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8689
21.7%
l 4812
12.0%
t 3753
9.4%
y 3187
 
7.9%
r 3013
 
7.5%
d 2409
 
6.0%
w 2302
 
5.7%
i 2292
 
5.7%
a 2251
 
5.6%
o 2077
 
5.2%
Other values (7) 5309
13.2%
Uppercase Letter
ValueCountFrequency (%)
N 2302
31.7%
R 1676
23.0%
P 936
12.9%
O 885
 
12.2%
U 616
 
8.5%
M 575
 
7.9%
C 283
 
3.9%
Space Separator
ValueCountFrequency (%)
3470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47367
93.2%
Common 3470
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8689
18.3%
l 4812
 
10.2%
t 3753
 
7.9%
y 3187
 
6.7%
r 3013
 
6.4%
d 2409
 
5.1%
N 2302
 
4.9%
w 2302
 
4.9%
i 2292
 
4.8%
a 2251
 
4.8%
Other values (14) 12357
26.1%
Common
ValueCountFrequency (%)
3470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8689
17.1%
l 4812
 
9.5%
t 3753
 
7.4%
3470
 
6.8%
y 3187
 
6.3%
r 3013
 
5.9%
d 2409
 
4.7%
N 2302
 
4.5%
w 2302
 
4.5%
i 2292
 
4.5%
Other values (15) 14608
28.7%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.0%
Missing1888
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean1921.6583
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:38.291960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile761.9
Q11457
median1828
Q32215
95-th percentile3187.1
Maximum10000
Range9911
Interquartile range (IQR)758

Descriptive statistics

Standard deviation767.16017
Coefficient of variation (CV)0.39921779
Kurtosis10.083066
Mean1921.6583
Median Absolute Deviation (MAD)372
Skewness1.8232285
Sum3679975.6
Variance588534.73
MonotonicityNot monotonic
2023-10-23T15:48:38.590155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 38
 
1.0%
1950 38
 
1.0%
2000 26
 
0.7%
1578 25
 
0.7%
2150 23
 
0.6%
1640 22
 
0.6%
2408 20
 
0.5%
1900 19
 
0.5%
1350 19
 
0.5%
1930 18
 
0.5%
Other values (583) 1667
43.8%
(Missing) 1888
49.6%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 2
0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)37.2%
Missing2070
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean2360.2414
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:38.903559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile246.4
Q11100
median1650
Q32399
95-th percentile4662
Maximum737147
Range737145
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation17719.603
Coefficient of variation (CV)7.5075385
Kurtosis1710.1077
Mean2360.2414
Median Absolute Deviation (MAD)642
Skewness41.21758
Sum4090298.4
Variance3.1398434 × 108
MonotonicityNot monotonic
2023-10-23T15:48:39.196277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 34
 
0.9%
2700 33
 
0.9%
900 28
 
0.7%
1600 26
 
0.7%
2000 25
 
0.7%
1300 25
 
0.7%
1700 23
 
0.6%
Other values (634) 1427
37.5%
(Missing) 2070
54.4%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)37.7%
Missing1859
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean2483.4669
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:39.492167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile348.3
Q1824
median1294
Q31786.25
95-th percentile2945.8
Maximum607936
Range607921
Interquartile range (IQR)962.25

Descriptive statistics

Standard deviation22375.239
Coefficient of variation (CV)9.0096787
Kurtosis627.83936
Mean2483.4669
Median Absolute Deviation (MAD)472
Skewness24.796084
Sum4827859.7
Variance5.0065133 × 108
MonotonicityNot monotonic
2023-10-23T15:48:39.770216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 36
 
0.9%
1600 36
 
0.9%
1200 32
 
0.8%
1500 30
 
0.8%
1350 28
 
0.7%
1650 28
 
0.7%
1450 23
 
0.6%
1300 23
 
0.6%
1000 22
 
0.6%
Other values (723) 1644
43.2%
(Missing) 1859
48.9%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 2
0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3082 
1
721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

Length

2023-10-23T15:48:40.070473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:40.319430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3082
81.0%
1 721
 
19.0%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
2446 
1
1357 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

Length

2023-10-23T15:48:40.528635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:41.159131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

Most occurring characters

ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2446
64.3%
1 1357
35.7%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3459 
1
 
344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

Length

2023-10-23T15:48:41.372485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:41.630206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3459
91.0%
1 344
 
9.0%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3140 
1
663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

Length

2023-10-23T15:48:41.932772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:42.385602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3140
82.6%
1 663
 
17.4%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
3382 
1
421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

Length

2023-10-23T15:48:42.722722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:43.199840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3382
88.9%
1 421
 
11.1%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
0
2509 
1
1078 
2
 
216

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3803
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

Length

2023-10-23T15:48:43.616945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T15:48:44.092159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3803
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2509
66.0%
1 1078
28.3%
2 216
 
5.7%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.947936
Minimum0
Maximum174
Zeros486
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-10-23T15:48:44.502589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3109
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)78

Descriptive statistics

Standard deviation52.821789
Coefficient of variation (CV)0.74451481
Kurtosis-0.85533655
Mean70.947936
Median Absolute Deviation (MAD)37
Skewness0.47028839
Sum269815
Variance2790.1414
MonotonicityNot monotonic
2023-10-23T15:48:45.023916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 486
 
12.8%
49 353
 
9.3%
174 196
 
5.2%
44 62
 
1.6%
38 58
 
1.5%
72 56
 
1.5%
165 55
 
1.4%
60 50
 
1.3%
37 49
 
1.3%
42 46
 
1.2%
Other values (151) 2392
62.9%
ValueCountFrequency (%)
0 486
12.8%
5 6
 
0.2%
6 6
 
0.2%
7 43
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 7
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.1%
ValueCountFrequency (%)
174 196
5.2%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 11
 
0.3%
165 55
 
1.4%
161 3
 
0.1%
160 28
 
0.7%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2023-10-23T15:48:22.426131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:55.717787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:58.450133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:01.086248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:04.967562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:07.616790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:10.226265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:12.755994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:16.316127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:19.871837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:22.688791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:56.154210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:58.698823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:01.333256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:05.219392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:07.893212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:10.470671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:13.003348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:17.019515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:20.117565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:22.962743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:56.416883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:58.963281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:01.653821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:05.473349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:08.149571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:10.721440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:13.486072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:17.435272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:20.373613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:23.212196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:56.648868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:59.207918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:02.039492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:05.732273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:08.390705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:10.975100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:13.715089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:17.840749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:20.624428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:23.495931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:56.919342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:59.488254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:02.389434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:06.029392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:08.657619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:11.240803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:14.002082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:18.314755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:20.889100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:23.772163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:57.181146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:59.767991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:02.804853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:06.293316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:08.935370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:11.502635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:14.255912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:18.593649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:21.151016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:24.020845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:57.433169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:00.022128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:03.168932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:06.540026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:09.180978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:11.737588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:14.493048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:18.852685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:21.399966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:24.276063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:57.683379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:00.286800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:03.564583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:06.811547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:09.429695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:11.989871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:14.742521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:19.087316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:21.646544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:24.542208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:57.941947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:00.564083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:04.209162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:07.069987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:09.698298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:12.260993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:15.256136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:19.363101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:21.884989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:24.811710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:47:58.187307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:00.826864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:04.578176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:07.337893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:09.967172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:12.501958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:15.689916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:19.591220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T15:48:22.145799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-23T15:48:45.350336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_arealuxury_scoreproperty_typebalconyfacingagePossessionstudy roomservant roomstore roompooja roomothersfurnishing_type
price1.0000.7430.7450.6830.7210.0030.7740.6040.6220.2170.5410.1360.0220.1000.2420.3680.3000.3340.0330.175
price_per_sqft0.7431.0000.2060.4110.406-0.1200.2860.1290.1430.0570.1990.0330.0000.0570.0290.0410.0000.0440.0350.021
area0.7450.2061.0000.6310.6920.1150.9490.8370.8060.2590.0290.0100.0220.0000.0180.0150.0390.0380.0420.042
bedRoom0.6830.4110.6311.0000.863-0.1000.8020.3850.5770.0620.5940.1750.0310.1290.1560.3170.2220.2920.0770.167
bathroom0.7210.4060.6920.8631.000-0.0060.8220.4680.6070.1810.4720.2250.0440.1110.1750.5180.2440.2830.0690.197
floorNum0.003-0.1200.115-0.100-0.0061.0000.1550.0880.1510.2230.4750.0790.0000.1240.0780.0800.1090.1000.0280.020
super_built_up_area0.7740.2860.9490.8020.8220.1551.0000.9270.8950.2271.0000.3040.0000.0860.1160.5870.0430.1540.0820.134
built_up_area0.6040.1290.8370.3850.4680.0880.9271.0000.9680.2900.0000.0001.0000.0000.0000.0000.0000.0000.0000.087
carpet_area0.6220.1430.8060.5770.6070.1510.8950.9681.0000.2360.0000.0250.0000.0000.0040.0000.0000.0000.0170.000
luxury_score0.2170.0570.2590.0620.1810.2230.2270.2900.2361.0000.3180.2230.0640.2560.1850.3470.2270.1910.1730.245
property_type0.5410.1990.0290.5940.4720.4751.0000.0000.0000.3181.0000.2100.0910.3700.1280.0700.2420.2540.0240.084
balcony0.1360.0330.0100.1750.2250.0790.3040.0000.0250.2230.2101.0000.0140.2710.1820.4390.1430.1950.0810.180
facing0.0220.0000.0220.0310.0440.0000.0001.0000.0000.0640.0910.0141.0000.0930.0000.0420.0330.0280.0000.053
agePossession0.1000.0570.0000.1290.1110.1240.0860.0000.0000.2560.3700.2710.0931.0000.1440.2930.1450.1890.1110.220
study room0.2420.0290.0180.1560.1750.0780.1160.0000.0040.1850.1280.1820.0000.1441.0000.1820.2220.3140.0320.142
servant room0.3680.0410.0150.3170.5180.0800.5870.0000.0000.3470.0700.4390.0420.2930.1821.0000.1610.2510.0000.275
store room0.3000.0000.0390.2220.2440.1090.0430.0000.0000.2270.2420.1430.0330.1450.2220.1611.0000.3050.1030.160
pooja room0.3340.0440.0380.2920.2830.1000.1540.0000.0000.1910.2540.1950.0280.1890.3140.2510.3051.0000.0340.218
others0.0330.0350.0420.0770.0690.0280.0820.0000.0170.1730.0240.0810.0000.1110.0320.0000.1030.0341.0000.053
furnishing_type0.1750.0210.0420.1670.1970.0200.1340.0870.0000.2450.0840.1800.0530.2200.1420.2750.1600.2180.0531.000

Missing values

2023-10-23T15:48:25.240150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-23T15:48:25.978619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-23T15:48:26.783919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature global park 4sector 360.827585.01081.0Super Built up area 1081(100.43 sq.m.)Carpet area: 650 sq.ft. (60.39 sq.m.)3.02.022.0NaNNew Property1081.0NaN650.00000008
1flatsmart world gemssector 890.958600.01105.0Carpet area: 1103 (102.47 sq.m.)2.02.024.0NaNNew PropertyNaNNaN1103.011000038
2flatpyramid elitesector 860.4679.058228.0Carpet area: 58141 (5401.48 sq.m.)2.02.010.0NaNUnder ConstructionNaNNaN58141.000000015
3flatbreez global hill viewsohna road0.325470.0585.0Built Up area: 1000 (92.9 sq.m.)Carpet area: 585 sq.ft. (54.35 sq.m.)2.02.0117.0NaNNew PropertyNaN1000.00585.000000049
4flatbestech park view sanskrutisector 921.608020.01995.0Super Built up area 1995(185.34 sq.m.)Built Up area: 1615 sq.ft. (150.04 sq.m.)Carpet area: 1476 sq.ft. (137.12 sq.m.)3.04.03+10.0North-WestRelatively New1995.01615.001476.0010011174
5flatsuncity avenuesector 1020.489022.0532.0Super Built up area 632(58.71 sq.m.)Carpet area: 532 sq.ft. (49.42 sq.m.)2.02.015.0North-EastRelatively New632.0NaN532.0001000159
6flatparas quartiergwal pahari7.5014018.05350.0Super Built up area 5350(497.03 sq.m.)4.04.03+20.0North-EastNew Property5350.0NaNNaN01011149
7flatexperion the heartsongsector 1082.008554.02338.0Super Built up area 2338(217.21 sq.m.)3.03.03+14.0EastRelatively New2338.0NaNNaN01000095
8flatadani m2k oyster grandesector 1021.909105.02087.0Super Built up area 1889(175.49 sq.m.)3.04.038.0North-EastRelatively New1889.0NaNNaN010000165
9houseindependentsector 1051.2010122.01186.0Plot area 1185.51(110.14 sq.m.)6.02.012.0North-WestOld PropertyNaN1185.51NaN0000009
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3793flatgls arawali homessohna road0.274687.0576.0Carpet area: 576 (53.51 sq.m.)2.02.021.0EastNew PropertyNaNNaN576.000000035
3794houseindependentsector 278.0026298.03042.0Plot area 338(282.61 sq.m.)9.09.034.0North-EastRelatively NewNaN3042.0NaN111102110
3795flateldeco accoladesohna road0.875965.01459.0Super Built up area 1457(135.36 sq.m.)Carpet area: 849 sq.ft. (78.87 sq.m.)2.02.03+10.0NaNRelatively New1457.0NaN849.010000072
3796flatparas dewssector 1060.926642.01385.0Super Built up area 1385(128.67 sq.m.)Built Up area: 940 sq.ft. (87.33 sq.m.)Carpet area: 845 sq.ft. (78.5 sq.m.)2.02.03+2.0EastRelatively New1385.0940.0845.0000000174
3797housesurendra homes dayaindependentd colonysector 60.7515625.0480.0Built Up area: 480 (44.59 sq.m.)4.04.021.0NaNUndefinedNaN480.0NaN0000000
3798flatpivotal devaansector 840.376346.0583.0Super Built up area 583(54.16 sq.m.)Carpet area: 483 sq.ft. (44.87 sq.m.)2.02.015.0North-WestRelatively New583.0NaN483.000000073
3799houseinternational city by sobha phase 1sector 1096.009634.06228.0Plot area 692(578.6 sq.m.)5.05.03+2.0South-WestRelatively NewNaN6228.0NaN111100160
3800flatansal api celebrity suitessector 20.608163.0735.0Super Built up area 735(68.28 sq.m.)1.01.015.0North-EastModerately Old735.0NaNNaN00000167
3801houseindependentsector 4315.5028233.05490.0Plot area 610(510.04 sq.m.)5.06.033.0EastModerately OldNaN5490.0NaN11110076
3802flatm3m ikonicsector 681.789128.01950.0Super Built up area 1950(181.16 sq.m.)Built Up area: 1845 sq.ft. (171.41 sq.m.)Carpet area: 1530 sq.ft. (142.14 sq.m.)3.03.03+27.0SouthRelatively New1950.01845.01530.0000001126

Duplicate rows

Most frequently occurring

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score# duplicates
0flatambience caitrionasector 2414.00200000.0700.0Built Up area: 700 (65.03 sq.m.)4.05.033.0EastUndefinedNaN700.0NaN00000002
1flatansal heights 86sector 860.905325.01690.0Built Up area: 1690 (157.01 sq.m.)3.03.0210.0NaNNew PropertyNaN1690.0NaN000000292
2flatansal heights 86sector 861.304666.02786.0Super Built up area 2786(258.83 sq.m.)4.06.0211.0EastNew Property2786.0NaNNaN010010862
3flatansal housing highland parksector 1030.886429.01369.0Super Built up area 1361(126.44 sq.m.)2.02.033.0NaNNew Property1361.0NaNNaN000000522
4flatantriksh heightssector 840.855556.01530.0Super Built up area 1350(125.42 sq.m.)2.02.0310.0North-WestNew Property1350.0NaNNaN100010242
5flatapartmentsector 920.754687.01600.0Carpet area: 1600 (148.64 sq.m.)3.04.032.0EastModerately OldNaNNaN1600.01000001132
6flatashiana anmolsohna road0.8811125.0791.0Super Built up area 1275(118.45 sq.m.)Carpet area: 791 sq.ft. (73.49 sq.m.)2.02.0213.0EastRelatively New1275.0NaN791.00000021272
7flatassotech blithsector 990.926739.01365.0Super Built up area 1365(126.81 sq.m.)2.02.03+22.0NaNUnder Construction1365.0NaNNaN000000562
8flatassotech blithsector 991.906702.02835.0Built Up area: 2835 (263.38 sq.m.)4.04.03+2.0North-EastUndefinedNaN2835.0NaN000000512
9flatats tourmalinesector 1092.308897.02585.0Super Built up area 2585(240.15 sq.m.)3.04.03+10.0EastNew Property2585.0NaNNaN010010742